The primary safety endpoint is the composite of major bleeding or clinically relevant non-major bleeding at 12 months.

This study will evaluate the efficacy and safety of apixaban 2.5 mg twice daily plus aspirin compared with DAPT (clopidogrel plus aspirin) in patients with CLI undergoing endovascular infrapopliteal revascularization and might prove the concept of an alternative antithrombotic regimen for these patients to be tested in a future large randomized clinical trial.

This study will evaluate the efficacy and safety of apixaban 2.5 mg twice daily plus aspirin compared with DAPT (clopidogrel plus aspirin) in patients with CLI undergoing endovascular infrapopliteal revascularization and might prove the concept of an alternative antithrombotic regimen for these patients to be tested in a future large randomized clinical trial.Identification of risk factors for antibiotic treatment failure is urgently needed in acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Here we investigated the relationship between sputum microbiome and clinical outcome of choice of initial antibiotics during hospitalization of AECOPD patients. Sputum samples of 41 AECOPD patients and 26 healthy controls were collected from Guangzhou Medical University, China. Samples were processed for 16S rRNA gene-based microbiome profiling. Thirty patients recovered with initial antibiotic treatment (antibiotic success or AS), while 11 patients showed poor outcome (antibiotic failure or AF). Substantial differences in microbiome were observed in AF versus AS patients and healthy controls. There was significantly decreased alpha diversity and increased relative abundances of Pseudomonas, Achromobacter, Stenotrophomonas and Ralstonia in AF patients. Conversely, Prevotella, Peptostreptococcus, Leptotrichia and Selenomonas were depleted. The prevalence of Selenomonas was markedly reduced in AF versus AS patients (9.1 % versus 60.0 %, P = 0.004). The AF patients with similar microbiome profiles in general responded well to the same new antibiotics in the adjusted therapy, indicating sputum microbiome may help guide the adjustment of antibiotics. Random forest analysis identified five microbiome operational taxonomic units together with C-reactive protein, procalcitonin and blood neutrophil count showing best predictability for antibiotic treatment outcome (area under curve 0.885). Functional inference revealed an enrichment of microbial genes in xenobiotic metabolism and antimicrobial resistance in AF patients, whereas genes in DNA repair and amino acid metabolism were depleted. Sputum microbiome may determine the clinical outcome of initial antibiotic treatment and be considered in the risk management of antibiotics in AECOPD.

We aimed to investigate the effect of chromium supplementation on glycemic control indices in patients with type 2 diabetes (T2DM).

Randomized controlled trials examining the effect of chromium supplementation on glycemic control indices and published before February 2020 were detected by searching online databases, including PubMed, Scopus, Embase, Web of sciences and The Cochrane Library, using a combination of suitable keywords. Mean change and standard deviation (SD) of the outcome measures were used to estimate the mean difference between the supplementation group and the control group at follow-up.

Twenty-eight studies reported fasting plasma glucose (FPG), insulin, hemoglobin A1C (HbA1C) and homeostatic model assessment for insulin resistance (HOMA-IR) as an outcome measure. Results revealed significant reduction in FPG (weighted mean difference (WMD) -19.00 mg/dl, 95% CI -36.15, -1.85, P = 0.030; I

99.8%, p < 0.001), insulin level (WMD -12.35 pmol/l, 95% CI -17.86, -6.83, P < 0.001), HbA1C (WMD -0.71 %, 95% CI -1.19, -0.23, P = 0.004) and HOMA-IR (WMD -1.53, 95% CI -2.35, -0.72, P < 0.001; I

89.9%, p < 0.001) after chromium supplementation.

The results of the current meta-analysis study might support the use of chromium supplementation for the improvement of glycemic control indices in T2DM patients.

The results of the current meta-analysis study might support the use of chromium supplementation for the improvement of glycemic control indices in T2DM patients.

To explore uric acid (UA) trajectories in different body mass index (BMI) populations and to determine their associations with incident diabetes.

A total of 4566 adults without diabetes in 2011 were enrolled. All participants underwent a medical examination every year until 2016, and were classified into three subgroups based on BMI non-obese (BMI < 24 kg/m

); overweight (BMI ≥ 24 kg/m

but < 28 kg/m

); and obese (BMI ≥ 28 kg/m

). Distinct UA trajectories were identified through group-based trajectory modelling (GBTM). RG-7112 price Cox proportional-hazards models were applied to evaluate the associations between UA trajectories and risk of incident diabetes.

UA trajectories were identified in the three BMI subgroups ‘low’ (42.4% in non-obese, 22.1% in overweight, 22.0% in obese); ‘moderate’ (32.5%, 41.1%, 34.8%); ‘moderate-high’ (18.6%, 29.5%, 30.8%); and ‘high’ (6.5%, 7.3%, 12.4%). After a 5-year follow-up, 170 (3.7%) participants had developed diabetes. The prevalence of new-onset diabetes increased progressively with the higher UA trajectories in the BMI groups (P values < 0.05). Whereas compared with the low trajectory, a significant association between a high UA trajectory and incidence of diabetes was observed only in the overweight population [hazard ratio (HR) 6.95, 95% confidence interval (CI) 1.90-25.45], with no significant associations found in either the non-obese (HR 0.67, 95% CI 0.13-3.52) or obese (HR 0.40, 95% CI 0.06-2.64) populations, in the fully adjusted model.

Higher UA trajectories are significantly associated with an increased risk of incident diabetes, thereby suggesting that monitoring UA trajectories over time may assist in the identification of prediabetes and diabetes, particularly in the overweight population.

Higher UA trajectories are significantly associated with an increased risk of incident diabetes, thereby suggesting that monitoring UA trajectories over time may assist in the identification of prediabetes and diabetes, particularly in the overweight population.